1. Field of the Invention
The present invention pertains to identifying objects in three-dimensional data and, more particularly, an automatic target recognition system employing a fuzzy logic process to identify targets in LADAR data.
2. Description of the Prior Art
Automatic target recognition ("ATR") systems identify objects represented in two-dimensional or three-dimensional data to determine whether they are potential targets. Three-dimensional data may be acquired in numerous ways and laser detection and ranging ("LADAR") systems are commonly employed for this purpose. In such systems, a laser signal is transmitted from a platform and, upon encountering an object, is reflected back to the platform. The platform can then process the reflected signal to obtain three-dimensional data regarding the object causing the reflection. Typically, this data includes a number of the object's features such as its height, length, and width. However, the platform typically transmits many laser signals across a general area that will contain a number of objects reflecting the signals. Thus, it is necessary for the ATR system to examine the data to see which reflecting objects might be of interest.
ATR systems are often divided into four subsystems: detection, segmentation, feature extraction, and identification. Identification can be described as the final process which takes inputs such as the features discussed above and establishes an identity for the object based on previously determined features of known objects. The accuracy of the identification depends on several factors including the correctness of the object features used in the comparison and the number of known objects constituting potential identifications.
The ATR system would ideally be able to compare completely accurate feature measurements against those of all known objects to establish the unknown object's identity. However, identification is frequently hampered by poor measurements such that, even if one of the potential identifications is correct, a complete match cannot be recognized. For instance, a length measurement might be compromised if part of the unknown object is obscured by a wall or fence and a width measurement might be affected by the orientation of the object relative to the platform. ATR system design constraints for size and speed may also affect performance.
An ATR system must therefore, as a practical matter, quickly establish the best possible identity with minimal computing resources. However, conventional computing techniques are poorly suited to meet these design constraints. For instance, conventional computing resources classically analyze information in two states such as "yes" and "no", or "true" and "false." An incomplete match in the comparison phase of a suitable operational ATR system cannot easily be represented in two states and consequently requires extensive analysis to produce a useable answer. The extensive analysis, in turn, consumes time and computing resources. This consumption becomes more acute as the list of potential identifications becomes more comprehensive since there typically will be no or, at most, only a few complete matches. Conventional computational techniques are consequently poorly adapted to meet the various design constraints in practically implementing an ATR system and, as demand for improved system performance rises, become still less suited. Thus, there is a need for a new method for identifying objects in three-dimensional data.